English

Equation-free modeling of evolving diseases: Coarse-grained computations with individual-based models

Adaptation and Self-Organizing Systems 2009-11-10 v1 Populations and Evolution

Abstract

We demonstrate how direct simulation of stochastic, individual-based models can be combined with continuum numerical analysis techniques to study the dynamics of evolving diseases. % Sidestepping the necessity of obtaining explicit population-level models, the approach analyzes the (unavailable in closed form) `coarse' macroscopic equations, estimating the necessary quantities through appropriately initialized, short `bursts' of individual-based dynamic simulation. % We illustrate this approach by analyzing a stochastic and discrete model for the evolution of disease agents caused by point mutations within individual hosts. % Building up from classical SIR and SIRS models, our example uses a one-dimensional lattice for variant space, and assumes a finite number of individuals. % Macroscopic computational tasks enabled through this approach include stationary state computation, coarse projective integration, parametric continuation and stability analysis.

Keywords

Cite

@article{arxiv.nlin/0310011,
  title  = {Equation-free modeling of evolving diseases: Coarse-grained computations with individual-based models},
  author = {Jaime Cisternas and C. William Gear and Simon Levin and Ioannis G. Kevrekidis},
  journal= {arXiv preprint arXiv:nlin/0310011},
  year   = {2009}
}

Comments

16 pages, 8 figures

R2 v1 2026-07-22T18:11:33.373Z